Vol.12, No.1, February 2023.                                                                                                                                                                              ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Facial Skin Type Prediction Based on Baumann Skin Type Solutions Theory Using Machine Learning


Rosayanti Efata, Widya Indriani Loka, Natasha Wijaya, Derwin Suhartono


© 2023 Rosayanti Efata, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 12, Issue 1, Pages 96-103, ISSN 2217-8309, DOI: 10.18421/TEM121-13, February 2023.


Received: 22 September 2022.

Revised:   30 November 2022.
Accepted:  12 December 2022.
Published: 27 February 2023.




The lack of knowledge of different facial skin types is still a frequent problem in Indonesia. The purpose of this research is to build a facial skin type prediction system using machine learning to classify facial skin types based on Baumann Skin Type Solutionswhich provides information on different skin types and suitable skincare ingredients. The dataset is collected manually by distributing a questionnaire among Indonesian citizens. The prediction models are built using three machine learning methods namely SVM, XGBoost, and 1D-CNN, and compared using 5-fold stratified cross-validation. XGBoostachieved the best performance on facial skin type prediction and optimized through hyperparameter tuning using Bayesian Optimization with a result of 93.5% averaged F1-score.


Keywords –Skin type classification, Baumann Skin Type Solutions, XGBoost, SVM, 1D-CNN, Bayesian Optimization.



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